System and methods for segmentation and assembly of cardiac MRI images

ABSTRACT

A method and system for image segmentation systems and related methods of automatically segmenting cardiac MRI images using deep learning methods. One example method includes inputting MRI volume data from a MRI scanner, segmenting the MRI volume data with a whole volume segmentation analysis module, assembling the segmented MRI volume data into a 3D volume assembly with a 3D volume assembly module, determining the 3D volume assembly for anatomic plausibility with an anatomic plausibility analysis module, and outputting a final segmented 3D volume assembly.

TECHNICAL FIELD

The invention relates to image segmentation systems and related methodsof automatically segmenting cardiac MRI images using deep learningmethods.

BACKGROUND

Cardiovascular diseases (CVDs) are one of the most common forms of heartdisease, which is the leading cause of death in developed countries.Major advances have been made in cardiovascular research and practiceaiming to improve diagnosis and treatment of cardiac diseases as well asreducing the mortality of CVD. In light of the latter, Magneticresonance imaging (MRI) is the preferred non-invasive imaging techniqueutilized for qualitative and quantitative assessment of cardiac metricssuch as stroke volume, ejection fraction, and strain.

Segmentation of the cardiac MRI images is often required for thecalculation of cardiac metrics. Cardiac MRI image segmentationpartitions the image into a number of semantically (i.e., anatomically)meaningful regions, based on which quantitative measures can beextracted, such as the myocardial mass, wall thickness, left ventricle(LV) and right ventricle (RV) volume as well as ejection fraction (EF)etc. Typically, the anatomical structures of interest for cardiac imagesegmentation include the LV, RV, left atrium (LA), right atrium (RA),and coronary arteries. The quantitative measures extracted from thecardiac image segmentation are crucial in the calculation of the cardiacmetrics.

Currently, clinicians have been relying on manual approaches for cardiacMRI segmentation. It typically takes a trained expert approximately 20minutes to analyze images of a single subject at two time points of thecardiac cycle, end-diastole (ED) and end-systole (ES). This is timeconsuming, tedious, and prone to subjective errors. To overcome thelimitations of the manual approaches of cardiac MRI segmentation,convolutional neural networks have been used to achieve state of the artperformance on many segmentation tasks of cardiac MRI imagesegmentation. However, the predicted segmentations from present modelsbased on convolutional neural networks may lead to cardiac shapes thatare anatomically improbable and positions that are medicallyimplausible. The results from these present models are still unfit forday-to-day clinical use. Therefore, there is need for a computerized andautomated cardiac MRI segmentation solution that can address theabove-addressed limitations by reducing time and labor costs and byincreasing reliability and reproducibility of anatomically plausiblecardiac MRI segmentation.

SUMMARY

In one embodiment, a method for segmentation and assembly of cardiacmagnetic resonance imaging (MRI) images is provided. The method includesinputting MRI volume data from a MRI scanner, segmenting the MRI volumedata with a whole volume segmentation analysis module, assembling thesegmented MRI volume data into a 3D volume assembly with a 3D volumeassembly module, determining the 3D volume assembly for anatomicplausibility with an anatomic plausibility analysis module, andoutputting a final segmented 3D volume assembly.

In another embodiment, a system for segmenting and assembling cardiacmagnetic resonance imaging (MRI) images is provided. The system includesa MRI scanner for acquiring MRI volume data from a patient, a computerfor processing the MRI volume data with a method for segmentation andassembly of cardiac magnetic resonance imaging (MRI) images where themethod further includes inputting MRI volume data from a MRI scanner,segmenting the MRI volume data with a whole volume segmentation analysismodule, assembling the segmented MRI volume data into a 3D volumeassembly with a 3D volume assembly module, determining the 3D volumeassembly for anatomic plausibility with an anatomic plausibilityanalysis module, and outputting a final segmented 3D volume assembly,and a display screen to display the final segmented 3D volume assemblygenerated by the method.

DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example methods, and otherexample embodiments of various aspects of the invention. It will beappreciated that the illustrated element boundaries (e.g., boxes, groupsof boxes, or other shapes) in the figures represent one example of theboundaries. One of ordinary skill in the art will appreciate that insome examples one element may be designed as multiple elements or thatmultiple elements may be designed as one element. Furthermore, elementsmay not be drawn to scale.

FIG. 1A illustrates a flowchart of an exemplary method for cardiac MRIsegmentation in accordance with one illustrative embodiment.

FIG. 1B illustrates a schematic of the exemplary method for cardiac MRIsegmentation in accordance with FIG. 1A.

FIG. 2A illustrates a flowchart of an UNet convolutional networkarchitecture of the exemplary method for cardiac MRI segmentation inaccordance with FIG. 1A.

FIG. 2B illustrates a flowchart of an exemplary downsampling block ofthe UNet convolutional network architecture in accordance with FIG. 2A.

FIG. 2C illustrates a flowchart of an exemplary upsampling block of theUNet convolutional network architecture in accordance with FIG. 2A.

FIG. 3 illustrates a flowchart of an exemplary whole volume segmentationanalysis module of the exemplary method for cardiac MRI segmentation inaccordance with FIG. 1A.

FIG. 4 illustrates a flowchart of an exemplary 3D volume assembly ofsegmented cardiac MRI images module of the exemplary method for cardiacMRI segmentation in accordance with FIG. 1A.

FIG. 5 illustrates a flowchart of an exemplary anatomical plausibilityanalysis module of the exemplary method for cardiac MRI segmentation inaccordance with FIG. 1A.

FIG. 6A illustrates a schematic of an exemplary mask autoencoder used inthe training of the exemplary method for cardiac MRI segmentation inaccordance with FIG. 1A.

FIG. 6B illustrates a flowchart of a modified UNet convolutional networkarchitecture of the exemplary mask autoencoder in accordance with FIG.6A.

FIG. 6C illustrates a flowchart of an exemplary upsampling block of theUNet convolutional network architecture of the modified UNetconvolutional network architecture of the exemplary anatomicalplausibility analysis module in accordance with FIG. 6B.

FIG. 7 illustrates a schematic of an exemplary mask autoencoder losstraining scheme of the exemplary mask autoencoder in accordance withFIG. 6A.

FIG. 8A illustrates a schematic of an exemplary adversarial variationalautoencoder used in the training of the exemplary method for cardiac MRIsegmentation in accordance with FIG. 1A.

FIG. 8B illustrates a schematic of exemplary output masks of thedistribution of latent mask vectors after initial training with theexemplary adversarial variational autoencoder in accordance with FIG.8A.

FIG. 8C presents an exemplary method of rejection sampling utilized forthe exemplary output masks of the distribution of latent mask vectors inaccordance with FIG. 8B.

DETAILED DESCRIPTION

FIGS. 1A and 1B illustrate an exemplary method 100 and system 120 forcardiac MRI segmentation in accordance with one illustrative embodiment.The method 100 depicts the overview of an algorithm which accepts MRIvolume data 102 as input and has a central processing segment 110 thatincludes a whole volume segmentation analysis module 104, 3D volumeassembly module 106, and anatomic plausibility analysis module 108. Themethod 100 can be performed by a computer 126 connected to a MRI scanner124 used to acquire images from a patient 122. The computer includes adisplay screen 128 to display the anatomically plausible segmented MRIimages, as well as an assembled 3D volume of the segmented MRI images112 which is computed by the method 100. The MRI volume data 102comprises 2D slices of MRI heart volume 102 to the method 100 is atensor of dimensions (256,256,1) and the assembled 3D volume of thesegmented MRI images 112 is a tensor of dimensions (256,256,4). Theinput image of the MRI volume data 102 represents a single slice of theMRI scan at one point in time. The output, which is the assembled 3Dvolume of the segmented MRI images 112, is a one-hot labeling of eachpixel in the input image. The size of the last dimension is 4 torepresent the 4 classes of background, left ventricle, right ventricle,and myocardium.

In one embodiment, the automated algorithm of the computerized method100 of FIG. 1A utilizes the input MRI volume data 102 from real clinicalexams conducted with a MRI scanner 124 on patients 122. In theembodiment described above, the computerized method 100 was trained withMRI volume data 102 comprising short-axis MRI images from 4824 patients122. The input MRI volume data 102 comprises MRI images consisting of anannotated volume for the end diastole and end systole stages. Annotatedsegments of the MRI images generally include a left ventricle, rightventricle, and myocardium.

FIGS. 2A-2C illustrates a flowchart 200 of an illustrative UNetconvolutional network architecture, an example downsampling block 204,and an example upsampling block 206 of the example method 100 forcardiac MRI segmentation in accordance with FIG. 1A. In theabove-referenced embodiment, FIG. 2A presents an exemplary flowchart ofthe UNet convolutional network architecture 200 of the centralprocessing segment 110. The UNet convolutional network architecture 200includes an input 202, convolutional down sampling layers 204, expandingup sampling layers 206 and a classification block 208. In theup-sampling layers 206, feature maps from early layers of the same sizeare concatenated.

FIG. 2B illustrates a flowchart of the exemplary downsampling block 204of the UNet convolutional network architecture 200 as presented in FIG.2A. In this exemplary embodiment, the down sampling block 204 includesan input 210 and is parametrized with one nb_filters parameter thatspecifies the number of filters used in the convolution layers 212 andlast layer of the block. The ordering of the layers in the downsamplingblock 204 is a convolutional layer 212A with activation function appliedfollowed by batch normalization 214A and dropout 216A. This is followedby another convolutional layer 212B (with activation function), batchnormalization 214B, dropout 216B and an output data pool 218. Thepurpose of the downsampling block 204 is to expand feature maps andgradually position each feature to where it lies in an original image ofthe input 210.

FIG. 2C illustrates a flowchart of an exemplary upsampling block 206 ofthe UNet convolutional network architecture 200 in accordance with FIG.2A. In this exemplary embodiment, the upsampling block 206 includes aninput 220 takes in two layers and a number specifying a number offilters as arguments. First the immediately preceding layer is upsampled through nearest neighbor interpolation to increase the size andput through a convolutional layer 224A. Then that layer is processed ina concatenating module 226 along with a convolution layer output 228from the downsampling block 204 with the same image dimensions. This isfollowed by two additional convolution layers 224B, C. The finalconvolutional layer 208 comprises a 1×1 kernel and a number of filtersequal to the number of classes. In this way, each pixel of the inputimage is classified into one of the classes in the classification block208 succeeding the upsampling block 206.

FIG. 3 illustrates a flowchart 300 of the exemplary whole volumesegmentation analysis module 104 of the example method 100 for cardiacMRI segmentation in accordance with FIG. 1A. The input MRI volume data102 is analyzed by the whole volume segmentation analysis module 300 isassessed as a whole volume to first determine if there is whole volumesegmentation 302. If it is determined that the input MRI volume data 102is not segmented as a whole, the whole volume segmentation analysismodule 300 proceeds to read the input MRI volume data 102 as a single 2DMRI slice at a time 304 following which the 2D MRI slice is subject to aresizing module 306. In the resizing module 306, the 2D MRI slices ofthe input MRI volume data 102 were resized with interpolation to matchthe input dimensions of the whole volume segmentation analysis module104. Alternatively, the 2D MRI slices of the input MRI volume data 102than the input dimensions, then a crop of the 2D MRI slices of the inputMRI volume data 102 is selected. If the 2D MRI slices of the input MRIvolume data 102 is smaller than the input dimensions, then the remainingspace is padded with zeroes such that the padded image matches the modeldimensions.

In one embodiment, the input MRI volume data 102 is augmented by theresizing module 306 to increase the number of images used for training.One method used for augmenting the data is to adjust the scale of theinput 2D MRI slices of the input MRI volume data 102. A random scalefactor from 0.9 to 1.1 is chosen at which to resize the 2D MRI slice.Then on the rescaled 2D MRI slice, a random cropped selection would bechosen if the rescaled 2D MRI slice was larger than the input or zeropadding applied if the rescaled 2D MRI slice was smaller. The input MRIvolume data 102 is further augmented by randomly adding flips,rotations, and translations to the 2D MRI slices. The input MRI volumedata 102 may also be further augmented by randomly adding noise. Everysingle 2D MRI slice is processed by the whole volume segmentationanalysis module 104 until it is determined to have whole volumesegmentation 302.

FIG. 4 illustrates a flowchart 400 of the exemplary 3D volume assemblyof segmented cardiac MRI images module 106 of the example method 100 forcardiac MRI segmentation in accordance with FIG. 1A. Following theprocessing of the input MRI volume data 102 by the whole volumesegmentation analysis module 104, the input MRI volume data 102 furtherprocessed via the 3D volume assembly of segmented cardiac MRI imagesmodule 106. The 2D MRI slices of the input MRI volume data 102 isassembled into a segmented 3D volume with a preliminary assembly module402. Due to noise in the 2D MRI slices of the input MRI volume data 102,several cardiac chambers may be falsely detected by the 3D volumeassembly module 106. A large component identification module 404 is thenutilized to identify the largest/tallest component of each class as truesegmentation and zero out other components. In one embodiment, thetallest/largest component, defined as the component spanning the mostconsecutive slices along the z-axis, is selected as the true componentfor each of the cardiac chambers. However, if there are multiplecomponents with the same tallest height or multiple components withheights that differ by less than 3, then the component with the largestnumber of voxels is selected from the group of components with thelargest heights. A chamber inspection module 406 is used to fill anyholes in any of the assembled volumes of the heart chambers. A hole isdefined as a small connected component of one class, such as a connectedcomponent for left ventricle, which is wholly surrounded by pixels ofanother class, such as myocardium. In this case, the pixels for leftventricle would be replaced with that for myocardium, thus filling thehole in the myocardium. Following this an assembled segmented 3D volumeis created as an output 408.

FIG. 5 illustrates a flowchart 500 of the exemplary anatomicalplausibility analysis module 108 of the example method 100 for cardiacMRI segmentation in accordance with FIG. 1A. Further to the 3D volumeassembly of segmented cardiac MRI images module 106, the output 408 isanalyzed for anatomic plausibility with the anatomical plausibilityanalysis module 108. The output 408 is analyzed for entire segmentationanatomic plausibility with a preliminary anatomic plausibility module502. Following the processing with the preliminary anatomic plausibilitymodule 502, the output 408 is read one 2D MRI slice 504 at a time andassessed for anatomic plausibility 506 for each slice. The 2D MRI slicethat was determined to not be anatomically plausible are subject to aslice segmentation module 508 and the code for the 2D MRI slicesegmentation is decoded and replaced with the anatomically plausiblesegmentation for the 2D MRI slice 510. This process is repeated forevery 2D MRI slice with the anatomical plausibility analysis module 108until the segmented cardiac 3D volume is entirely anatomicallyplausible.

FIGS. 6A-6C illustrate a schematic of an exemplary mask autoencoder 600,a flowchart of a modified UNet convolutional network architecture 620, aflowchart of an exemplary upsampling block 630 of the UNet convolutionalnetwork architecture of the modified UNet convolutional networkarchitecture used in the training of the exemplary method 100 forcardiac MRI segmentation in accordance with FIG. 1A. As shown in FIG.6A, the mask autoencoder 600 is trained to encode segmentation masksinto a lower-dimensional space to aid in constraining segmentation masksto be anatomically plausible. In the exemplary embodiment, an inputoriginal mask 602 to the mask autoencoder 600 is a multi-channel one-hotencoded label mask that is processed by an encoder 604, code 608, and adecoder 610 to produce an output reconstructed mask 612 with channelsequal to the number of label classes.

FIG. 6B illustrates a flowchart of a modified UNet convolutional networkarchitecture 620 of the exemplary mask autoencoder 600 in accordancewith FIG. 6A. The modified UNet convolutional network architecture 620of the exemplary anatomical plausibility analysis module 108 includes aninput 622, convolutional down sampling layers 624, expanding up samplinglayers 628 and a classification block 630. Additionally, as part of themask autoencoder 600, the modified UNet convolutional networkarchitecture 620 also includes a flattening layer 626 that lead to aflattened code, which can be used to produce the output reconstructedmask 612 with channels equal to the number of label classes.Additionally, modified UNet convolutional network architecture 620 alsoincludes a modified upsampling block 640 as presented in FIG. 6C. Inthis exemplary embodiment, the modified upsampling block 640 includes aninput 642 with a upsampling layer 644, followed by two convolutionlayers 646 A,B with each have succeeding batch normalization modules 648A,B. The modified upsampling block 640 further differ in that there areno longer skip connections from earlier blocks into later blocks. Thislack of skip connections is due to requirement of the decoder 610 themask autoencoder 600 to decode from the flattening layer 626 alonewithout using any of the previous layers before the flattening layer626.

FIG. 7 illustrates a schematic of an exemplary mask autoencoder losstraining scheme 700 of the exemplary mask autoencoder 600 in accordancewith FIG. 6A. The exemplary mask autoencoder 600 is trained with across-entropy loss function. The cross-entropy loss function is weightedsuch that rarer class examples contribute more to the loss than examplesfrom common classes. In one embodiment, the cross-entropy loss functionused is:

${{Heart}\mspace{14mu}{Segmentation}\mspace{14mu}{Minibatch}\mspace{14mu}{Loss}} = {\sum\limits_{i\text{-}{pixels}\mspace{14mu}{in}\mspace{14mu}{minibatch}}( {{w_{background}*y_{background}^{(i)}*{\log( {{model\_ pred}( x^{(i)} )_{background}} )}} + {w_{leftventricle}*y_{leftventricle}^{(i)}*{\log( {{model\_ pred}( x^{(i)} )_{leftventricle}} )}} + {w_{rightventricle}*y_{rightventricle}^{(i)}*{\log( {{model\_ pred}( x^{(i)} )_{rightventricle}} )}} + {w_{myocardium}*y_{myocardium}^{(i)}*{\log( {{model\_ pred}( x^{(i)} )_{myocardium}} )}}} )}$where the weights are respectively 0.01, 0.4, 0.4, 0.19 for background,right ventricle, myocardium, and left ventricle and (y^((i))_(background), y^((i)) _(left ventricle), y^((i)) _(myocardium), y^((i))_(right ventricle)) is a one-hot encoded vector for the class label ofthe i-th pixel. ADAM optimizer with a learning rate of 0.0001 was usedto optimize the model over 500 epochs. Training was done over 500epochs.

In another embodiment presented in the disclosure presented in FIG. 7 ,the mask autoencoder loss training scheme 700 is used to preventanatomically implausible segments of predicted masks 704 by utilizing anautoencoder system 702 utilizing encoders 708 pre-trained on aground-truth mask 706 and incorporating an autoencoder loss incorporatedin codes 710 during training. The autoencoder loss code 710 used is:AutoencoderLoss=MSE(encoded(segmentation),encoded(groundtruth))

The autoencoder system 702 has been pre-trained to encode segmentationmasks of MRI images in a low-dimensional representation, so similarsegmentations should have similar codes when encoded by the autoencodersystem 702. Additionally, autoencoder codes 710 contain fewer dimensionsthan the input MRI images, similar representations may be mapped to thesame autoencoder code 710 by the encoder 708 to calculate a mean squareerror 712 based on which noise should be removed. In the regularizedtraining scheme, the segmentation model is trained using a loss functiondefined by:Loss=A*SegmentationLoss+B*AutoencoderLoss

As training progresses, the A parameter decreases from 1 to 0 and the Bparameter increases from 0 to 1.

In another embodiment, the anatomic plausibility module 108 is trainedwith a generative model, such as an adversarial variational autoencoder(aVAE) 800 as presented in FIG. 8A. The aVAE 800 is trained on cardiacsegmentation masks to create a smooth manifold of latent codes that,when run through the autoencoder's decoder, have valid decodings toplausible cardiac segmentations. The aVAE 800 is trained in anadversarial manner, alternately training the aVAE 800 with an exemplarymask autoencoder 600 as presented in FIG. 6 and then training theencoder 803 and a discriminator 806 combined network in an adversarialmanner. The discriminator 806 is a binary discriminator that is trainedto distinguish true codes that come from the distribution p(z|x) of theVAE's encoder 803 and decoder 811 and false codes generated from anormal distribution 802 that is concatenated with label information. Thediscriminator 806 is a neural network that takes in a vector 801 withdimensions equal to the VAE latent code 805 and outputs 809 a binarylabel of either 0 or 1. The adversarial training is done by sampling adata set z from the distribution p(z|x) of the encoder 803 and thenpassing z through the discriminator 806. The combined encoderdiscriminator 806 is then trained through backpropagation with theoutput label as 1 for true 808. Then another sample z′ 804 is drawn fromthe normal distribution 802 and adjoined with label information 807.Following this sample z′ 804 is passed through the discriminator 806 andthe discriminator is trained through backpropagation to predict 0 forfalse results 808.

FIG. 8B shows output masks 810 of the distribution of latent vectors inthe latent space of the aVAE 800 after initial training of the aVAE 800on approximately 20000 short-axis masks with and without augmentationwith additional vectors. Output mask of training without augmentation812 presents a good, well-populated latent space of latent codes forvalid cardiac segmentations, providing one valid latent code for eachshort-axis mask used in training. Further methods of augmentation areemployed to expand the number of valid latent codes and densely populatethe latent space as depicted by the output mask with augmentation 814.

FIG. 8C present a schematic of the rejection sampling method 820utilized for augmentation and densification of the output mask 814 aspresented in FIG. 8B. The method of rejection sampling 820 includes anapproximation step 822 to generate a distribution of new latent codesthat approximates a distribution P(z) of 20000 valid latent codes. Inone embodiment, the approximation step 822 utilizes a Parzen windowsapproach to produce the approximated distribution. A proposaldistribution 824 whose support includes the support of an approximateddistribution, Q(z) is then chosen. In one embodiment, the rejectionsampling method 820 utilizes custom crafted features 826 to determinethe validity of a segmentation, such as whether the shape of a heartchamber exhibits an extreme concavity or whether the right ventricle andmyocardium overlap. The rejection sampling method 820 uses a generativeadversarial network (GAN) to compare the results of the custom craftedfeatures 826 to generate masks and discriminate between generated andreal masks and determine the validity of the decoded mask 828. Theencoder 604 of the aVAE 800 further encodes the given segmentation intothe latent space of the output masks 830. Then the closest latent codethat decodes to a valid mask is found for the latent code of the givensegmentation 832.

References to “one embodiment”, “an embodiment”, “one example”, and “anexample” indicate that the embodiment(s) or example(s) so described mayinclude a particular feature, structure, characteristic, property,element, or limitation, but that not every embodiment or examplenecessarily includes that particular feature, structure, characteristic,property, element or limitation. Furthermore, repeated use of the phrase“in one embodiment” does not necessarily refer to the same embodiment,though it may.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

Throughout this specification and the claims that follow, unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood to beterms of inclusion and not exclusion. For example, when such terms areused to refer to a stated integer or group of integers, such terms donot imply the exclusion of any other integer or group of integers.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

While example systems, methods, and other embodiments have beenillustrated by describing examples, and while the examples have beendescribed in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the systems, methods, and other embodiments described herein.Therefore, the invention is not limited to the specific details, therepresentative apparatus, and illustrative examples shown and described.Thus, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims.

What is claimed is:
 1. A method for segmentation and assembly of cardiacmagnetic resonance imaging (MRI) images, the method comprising:inputting MRI volume data from a MRI scanner; segmenting the MRI volumedata with a whole volume segmentation analysis module; assembling thesegmented MRI volume data into a 3D volume assembly with a 3D volumeassembly module; determining the 3D volume assembly for anatomicplausibility with an anatomic plausibility analysis module; andoutputting a final segmented 3D volume assembly, wherein the 3D volumeassembly module further comprises a large component identificationmodule to identify a largest connected component of each class as truesegmentation and to zero out other components.
 2. The method accordingto claim 1, wherein the whole volume segmentation analysis modulefurther comprises a resizing module for resizing 2D MRI slices of theinput MRI volume data with interpolation to match input dimensions ofthe input MRI volume data.
 3. The method according to claim 1, whereinthe 3D volume assembly module further comprising a chamber inspectionmodule to fill holes of assembled 3D volume output of the largecomponent identification module.
 4. The method according to claim 1,wherein the method for segmentation and assembly of cardiac magneticresonance imaging (MRI) images is trained with an adversarialvariational autoencoder.
 5. The method according to claim 4, wherein theadversarial variational autoencoder comprises a rejection samplingmethod for augmentation and densification of the 3D volume assembly. 6.A method for segmentation and assembly of cardiac magnetic resonanceimaging (MRI) images, the method comprising: inputting MRI volume datafrom a MRI scanner; segmenting the MRI volume data with a whole volumesegmentation analysis module; assembling the segmented MRI volume datainto a 3D volume assembly with a 3D volume assembly module; determiningthe 3D volume assembly for anatomic plausibility with an anatomicplausibility analysis module; and outputting a final segmented 3D volumeassembly, wherein the anatomic plausibility analysis module furthercomprises a slice segmentation module for segmenting the 2D MRI slicesof the input MRI volume data determined to not be anatomicallyplausible.
 7. The method according to claim 6, wherein the slicesegmentation module comprises: an encoder for encoding an input originalmask; a code for processing of the encoded input original mask; and adecoder for outputting a reconstructed mask.
 8. A method forsegmentation and assembly of cardiac magnetic resonance imaging (MRI)images, the method comprising: inputting MRI volume data from a MRIscanner; segmenting the MRI volume data with a whole volume segmentationanalysis module; assembling the segmented MRI volume data into a 3Dvolume assembly with a 3D volume assembly module; determining the 3Dvolume assembly for anatomic plausibility with an anatomic plausibilityanalysis module; and outputting a final segmented 3D volume assembly,wherein the input MRI volume data is a tensor of dimensions (256, 256,1).
 9. A method for segmentation and assembly of cardiac magneticresonance imaging (MRI) images, the method comprising: inputting MRIvolume data from a MRI scanner; segmenting the MRI volume data with awhole volume segmentation analysis module; assembling the segmented MRIvolume data into a 3D volume assembly with a 3D volume assembly module;determining the 3D volume assembly for anatomic plausibility with ananatomic plausibility analysis module; and outputting a final segmented3D volume assembly, wherein the final segmented 3D volume assembly istensor of dimensions (256, 256, 4).
 10. The method according to claim 9,wherein a last dimension of the final segmented 3D volume assemblyrepresents classes of background, left ventricle, right ventricle, andmyocardium.
 11. A system for segmenting and assembling cardiac magneticresonance imaging (MRI) images, the system comprising a processor and anon-transitory computer-readable storage medium storing instructionthat, when executed by the processor, cause the processor to perform amethod, the method comprising: receiving MRI volume data from a MRIscanner; segmenting the MRI volume data with a whole volume segmentationanalysis module; assembling the segmented MRI volume data into a 3Dvolume assembly with a 3D volume assembly module; determining the 3Dvolume assembly for anatomic plausibility with an anatomic plausibilityanalysis module; and generating a final segmented 3D volume assembly;wherein the 3D volume assembly module further comprises a largecomponent identification module to identify a largest connectedcomponent of each class as true segmentation and to zero out othercomponents.
 12. The system according to claim 11, wherein the wholevolume segmentation analysis module further comprises a resizing modulefor resizing 2D MRI slices of the input MRI volume data withinterpolation to match input dimensions of the input MRI volume data.13. The system according to claim 11, wherein the 3D volume assemblymodule further comprising a chamber inspection module to fill holes ofassembled 3D volume output of the large component identification module.14. The system according to claim 11, wherein the method forsegmentation and assembly of cardiac magnetic resonance imaging (MRI)images is trained with an adversarial variational autoencoder.
 15. Thesystem according to claim 14, wherein the adversarial variationalautoencoder comprises a rejection sampling method for augmentation anddensification of the 3D volume assembly.
 16. A system for segmenting andassembling cardiac magnetic resonance imaging (MRI) images, the systemcomprising a processor and a non-transitory computer-readable storagemedium storing instruction that, when executed by the processor, causethe processor to perform a method, the method comprising: receiving MRIvolume data from a MRI scanner; segmenting the MRI volume data with awhole volume segmentation analysis module; assembling the segmented MRIvolume data into a 3D volume assembly with a 3D volume assembly module;determining the 3D volume assembly for anatomic plausibility with ananatomic plausibility analysis module; and generating a final segmented3D volume assembly, wherein the anatomic plausibility analysis modulefurther comprises a slice segmentation module for segmenting the 2D MRIslices of the input MRI volume data determined to not be anatomicallyplausible.
 17. The system according to claim 16, wherein the slicesegmentation module comprises: an encoder for encoding an input originalmask; a code for processing of the encoded input original mask; and adecoder for outputting a reconstructed mask.
 18. A system for segmentingand assembling cardiac magnetic resonance imaging (MRI) images, thesystem comprising a processor and a non-transitory computer-readablestorage medium storing instruction that, when executed by the processor,cause the processor to perform a method, the method comprising:receiving MRI volume data from a MRI scanner; segmenting the MRI volumedata with a whole volume segmentation analysis module; assembling thesegmented MRI volume data into a 3D volume assembly with a 3D volumeassembly module; determining the 3D volume assembly for anatomicplausibility with an anatomic plausibility analysis module; andgenerating a final segmented 3D volume assembly, wherein the input MRIvolume data is a tensor of dimensions (256, 256, 1).
 19. A system forsegmenting and assembling cardiac magnetic resonance imaging (MRI)images, the system comprising a processor and a non-transitorycomputer-readable storage medium storing instruction that, when executedby the processor, cause the processor to perform a method, the methodcomprising: receiving MRI volume data from a MRI scanner; segmenting theMRI volume data with a whole volume segmentation analysis module;assembling the segmented MRI volume data into a 3D volume assembly witha 3D volume assembly module; determining the 3D volume assembly foranatomic plausibility with an anatomic plausibility analysis module; andgenerating a final segmented 3D volume assembly, wherein the input MRIvolume data is a tensor of dimensions (256, 256, 4).
 20. The systemaccording to claim 19, wherein a last dimension of the final segmented3D volume assembly represents classes of background, left ventricle,right ventricle, and myocardium.